Investigation of a process simulation method for flexible clamping of sheet metal parts

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Procedia Engineering 207 (2017) 1599–1604

International International Conference Conference on on the the Technology Technology of of Plasticity, Plasticity, ICTP ICTP 2017, 2017, 17-22 17-22 September September 2017, 2017, Cambridge, United Kingdom Cambridge, United Kingdom

Investigation Investigation of of aa process process simulation simulation method method for for flexible flexible clamping clamping of of sheet sheet metal metal parts parts a a b Felix Felix Bauer Bauera,, Alexandra Alexandra Werber Werbera*, *, Marion Marion Merklein Merkleinb a aDaimler AG, Bela-Barenyi-Str.1, 71059 Sindelfingen, Germany Daimler AG, Bela-Barenyi-Str.1, 71059 Sindelfingen, Germany of Manufacturing Technology, Institute of Manufacturing Technology, Friedrich-Alexander-University Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen-Nürnberg, Egerlandstraße Egerlandstraße 13, 13, 91058 91058 Erlangen, Erlangen, Germany Germany

b bInstitute

Abstract Abstract Flexibility Flexibility in in the the body-in-white body-in-white shop shop can can be be increased increased with with the the use use of of aa running running clamping clamping technology, technology, which which enables enables clamping clamping with a welding robot during laser welding. Unlike common clamping fixtures, the running clamping technology with a welding robot during laser welding. Unlike common clamping fixtures, the running clamping technology is is operated operated force force controlled. controlled. A A closed closed gap gap between between flanges flanges is is necessary necessary to to achieve achieve aa proper proper weld weld seam. seam. The The objective objective of of the the presented presented FEFEsimulation simulation method method is is to to predict predict required required clamping clamping forces forces to to close close the the gap gap between between sheet sheet metal metal part part flanges flanges with with geometrical geometrical inaccuracies. inaccuracies. For For calibration calibration of of the the simulation simulation model, model, clamping clamping experiments experiments with with simple simple geometry geometry are are used. used. Steel Steel and and aluminum aluminum L-specimens are manufactured using air bending and clamped with the running clamping technology. Clamping L-specimens are manufactured using air bending and clamped with the running clamping technology. Clamping forces forces are are measured measured precisely precisely using using aa load load cell, cell, which which has has been been integrated integrated into into the the running running clamping clamping technology. technology. In In this this contribution contribution aa FEFEsimulation simulation method method for for process process analysis analysis of of the the flexible flexible clamping clamping process process with with the the running running clamping clamping technology technology is is investigated. investigated. For For clamping simulation, specimens are generated by air bending simulation, as well as by optical measurement of clamping simulation, specimens are generated by air bending simulation, as well as by optical measurement of experimentally experimentally manufactured manufactured specimens. specimens. Since Since specimens specimens are are bent bent during during clamping clamping process process in in the the opposite opposite direction direction of of the the previous previous bending bending process, influences of residual stresses and kinematic hardening on the required clamping forces are expected. process, influences of residual stresses and kinematic hardening on the required clamping forces are expected. Thus, Thus, in in FEFEsimulation simulation an an elastic-plastic elastic-plastic material material model model with with kinematic kinematic hardening hardening parameters parameters is is used. used. A A mapping mapping method method with with springback springback compensation compensation is is applied applied to to obtain obtain specimens specimens with with optically optically measured measured geometry geometry including including forming forming history history data data of of bending bending simulation. Simulation setups are investigated regarding results of clamping forces. Simulation results are verified simulation. Simulation setups are investigated regarding results of clamping forces. Simulation results are verified using using the the obtained obtained experimental experimental data. data.

© © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd.

© 2017 The Authors. by Elsevier Peer-review underPublished responsibility of the the Ltd. scientific committee committee of of the the International International Conference Conference on on the the Technology Technology of of Peer-review under responsibility of scientific Peer-review under responsibility of the scientific committee of the International Conference on the Technology of Plasticity. Plasticity.. Plasticity

Keywords: Keywords: finite finite element element method; method; clamping clamping simulation; simulation; forming forming history history

** Corresponding Corresponding author. author. Tel.: Tel.: +49-176-30912094; +49-176-30912094; fax: fax: +49-7031-9041656 +49-7031-9041656 E-mail E-mail address: address: [email protected] [email protected] 1877-7058 1877-7058 © © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. Peer-review the scientific scientific committee committee Peer-review under under responsibility responsibility of of the Plasticity Plasticity..

of of the the International International Conference Conference on on the the Technology Technology of of

1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Conference on the Technology of Plasticity. 10.1016/j.proeng.2017.10.1055

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Felix Bauer et al. / Procedia Engineering 207 (2017) 1599–1604 Felix Bauer et al. / Procedia Engineering 00 (2017) 000–000

1. Introduction Nowadays, automotive manufacturers face challenges of shorter product life-cycles, demands of higher vehicle varieties and more individualized vehicles, shorter lead-times and a rapidly changing demand of the market [1]. This makes flexibility in the cost-intensive body-in-white-shop very important [2]. A promising solution is the running clamping technology, which enables clamping with a welding robot during remote laser welding. Hereby, instead of using common clamping fixtures with stationary process clamps, clamping is realized by a clamping roll, connected to the remote laser welding robot. The common remote laser welding process is described in detail by Buehrle et. al. in [3]. Using running clamping technology, parts with different weld seam designs can be manufactured without any positioning of process clamps in the fixture. Furthermore, the welding fixture is getting less expensive and simpler, which improves accessibility for part handling equipment. The running clamping technology was first presented by Rippl in [4]. Clamping is operated force controlled. The clamping force is applied by a pneumatic cylinder and has to be sufficient to close the gap between part flanges. The quality of the weld seam is heavily influenced by the gap between part flanges [5]. However, excessive clamping forces may lead to unwanted deformations of clamped parts. Hence, the amount of clamping force should be well configured. This makes a proper design of joined parts and of the applied clamping force essential. For welding of real parts with running clamping technology clamping force is applied by a step size of 50 N. Process design can be significantly improved using finite element method for process simulation of clamping with running clamping technology. Required clamping forces dependent on geometrical inaccuracies of clamped parts can be determined. The design of clamping path and clamping fixture can be improved. Further, the design of parts can be adapted to the clamping process in an early design phase. The lead time of the laser welding process in the production line and the number of cost-intensive experiments can be reduced. In this contribution a FE-simulation method of the running clamping technology is investigated. The method has been presented in [6]. For clamping simulation, specimens are generated by air bending simulation, as well as by using optical measurements of experimentally manufactured specimens. Taking forming history data into account, an improvement of simulation results can be expected. Since specimens are bent during clamping process in the opposite direction of the previous air bending process, influences of residual stresses resulting from bending process and of the Bauschinger effect on the required clamping forces are expected. The Bauschinger effect, which describes the reduction of yield stress for a load reversal, was discovered by Bauschinger and published in [7]. The ratio between yield stress after load reversal and initial yield stress can be described by the Bauschinger coefficient α. The material behavior is characterized as kinematic-hardening and can be modeled by translating the yield surface using a backstress tensor. In material models of LS-DYNA kinematic hardening laws developed by Chaboche and Rousselier [8], as well as by Yoshida and Uemori [9] are available. In order to transfer forming history data between different spatial discretizations a mapping tool can be applied, as described in [10] or the keyword *INCLUDE_STAMPED_PART of LS-DYNA. When element stresses are mapped, the same geometry and material data have to be used, otherwise unwanted deformations might occur due to springback effects. This makes mapping of residual stresses from forming simulation on CAD or optical measured geometry critical, when precise geometry data is required. 2. Experimental setup Experiments with simple L-specimen geometry are chosen to validate the simulation method. L-specimens are manufactured by air bending of sheet metal blanks. They are made of CR340LA with a thickness of 1.24 mm and of AA5182 with a thickness of 1.98 mm, which both are frequently used in structural automotive parts. A sketch of the experimental setup is shown in Fig. 1 (a). In the clamping fixture L-specimens are positioned on a thin piezoelectric tactile pressure film sensor and clamped against the rear wall. The film sensor is fixed on the base blank and has a sensor width of 56 mm. L-specimens have a width of 240 mm. The pressure film sensor is located underneath the center of the L-specimen. Geometrical inaccuracy is realized by a bending angle of L-specimens higher than 90°. As visualized in Fig. 1 (b), first the L-specimen is mounted in the fixture, then the L-specimen is clamped force controlled by the clamping roll. If a closed gap is detected during the whole clamping process, the necessary



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clamping force is determined by the signal of the load cell, otherwise the pneumatic clamping force is increased by 20 N. Experiments are performed with three repetitions for each nominal bending angle and material. b

a

mounting of L-specimen

clamping (force controlled)

closed gap

increasement of clamping force by 20 N

yes

required clamping force for closed gap

no

Fig. 1. (a) Sketch of the experimental setup; (b) experimental workflow

3. FE-simulation setup For both materials LS-DYNA material card 133 *MAT_BARLAT_YLD_2000 is used. Parameters required for these cards are recorded in tensile, bulge and cyclic bending tests. Kinematic hardening is modeled using Chaboche’s formulation. This formulation requires far less time to identify hardening parameters, compared to Yoshida and Uemori’s hardening law with same result quality [11]. Chaboche parameters for material cards are gained by inverse parameter identification using cyclic bending tests, whose load cases are similar to investigated Lspecimens. Inverse parameter identification using cyclic bending tests is described in detail in [12]. As pictured in Fig. 2, for FE-simulation in LS-DYNA R9.0 four different ways of modeling specimens are applied for clamping simulation. Specimens are generated using optical 2D profile measurements of experimental specimens (I), as well as using bending FE-simulation (III). Penetration depth of the bending punch is adjusted in order to match bending angles of numerically manufactured specimens with measurement based specimens. To investigate the influence of forming history data and to compare them with measurement based specimens, numerically manufactured specimens are also used without forming history data (IV). Further, forming history data from bending simulation is mapped on measurement based specimens (II) using SIMAN-Mapper by inpro. After mapping stresses, strains and shell thickness, a springback-compensation is performed. The part with mapped forming history data undergoes springback simulations, where resultant nodal displacement is evaluated. As long as resultant nodal displacement exceeds a user defined limit (0.001 mm), initial stresses are reduced. In this way stresses can be mapped without generating artificial deformations of the target part. specimen generation measured geometry

I measured geometry with mapped history data

II

effective plastic strain [mm] 0.220 0.198 0.176 0.154 0.132 0.110 0.088 0.066 0.044 0.022 forming 0.000

FE-simulation of clamping process Gravity (implicit static)

Closing tensioners (implicit static)

Clamping with roll (explicit dynamic)

geometry with history data

air bending simulation

III

IV

forming geometry

Fig. 2. FE-simulation setup; left specimen generation; right FE-simulation steps

During the first FE-simulation step specimens are positioned in the fixture, using gravity load in negative z- and y-direction. The second simulation step is closing of clamps towards the rear wall, with 1200 N applied on each

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clamp. Both simulation steps are calculated using implicit static time integration scheme. For dynamic clamping simulation with modeled running technology, explicit time integration scheme is applied. All parts are modeled by shell elements. Clamping roll is defined as rigid body, contact between roll and L-specimen is modeled frictionless by *CONTACT_FORMING_SURFACE_TO_SURFACE. Contact penalty option 2 (using master segment stiffness) is chosen. The displacement controlled clamping roll is moved downwards until the gap between Lspecimen and base blank is closed and subsequently traversed in x-direction. Clamping forces are analyzed by contact force output between roll and L-specimen. Further details regarding the clamping simulation model can be found in [6]. 4. Results During experiments the force signal of the load cell is recorded. Experimental results of clamping forces required to close the gap between L-specimen and base blank are evaluated using the mean value of measured clamping force in the region above the pressure film sensor, where the closed gap is detected. As presented in [13], with increasing geometrical inaccuracy higher clamping forces are required. Due to moment of inertia, aluminum specimens with 1.98 mm blank thickness require more clamping force, than steel specimens with 1.24 mm. Required clamping forces of simulation are compared with experimental results. Clamping forces are calculated for all specimens (three of each experimental configuration) using the four different simulation setups. Each simulation result is directly compared to the experimental result of the respective specimen. Fig. 3 shows the comparison of required clamping forces of simulation using geometry from bending simulation with forming history data (geo hist) and without (geo), as well as experimental results (exp). CR340LA specimens with a mean bending angle of 91.35° are investigated in Fig. 3 (a). Differences of clamping forces between the three specimens can be well observed, for both simulation setups and for experimental results. In Fig. 3 (b) AA5182 specimens with a mean bending angle of 91.35° are analyzed. In this case only slight differences of required clamping forces between the three specimens can be observed. The defined pneumatic force was the same. Maximum deviation of bending angle is detected between steel specimen 2 and 3 with 0.24° and between aluminum specimen 1 and 3 with 0.09°, leading to maximum deviation of bending force for these configurations. For steel specimens stronger influence of forming history data is determined. This can be attributed to stronger influence of the Bauschinger effect, which can be related to the Bauschinger coefficients α of materials and higher residual stresses. During material characterization an α value of CR340LA 0.41 and of AA5182 0.59 was recorded. Forming history data reduces clamping forces and shifts simulation forces very close to experiment, leading to a good agreement between simulation and experiment. b

CR340LA (1.24 mm) bending angle 91.35°

500 N 450 N 400 N 350 N 300 N 250 N

St 1 geo St 2 geo St 3 geo

200 N 0N 0 mm

St 1 geo hist St 2 geo hist St 3 geo hist

St 1 exp St 2 exp St 3 exp

50 mm 100 mm 150 mm 200 mm x - coordinate of clamping roll

250 mm

required clamping force

required clamping force

a

AA5182 (1.98 mm) bending angle 91.35°

600 N

550 N

500 N Alu 1 geo Alu 2 geo Alu 3 geo

450 N 0N 0 mm

Alu 1 geo hist Alu 2 geo hist Alu 3 geo hist

Alu 1 exp Alu 2 exp Alu 3 exp

50 mm 100 mm 150 mm 200 mm x - coordinate of clamping roll

250 mm

Fig. 3. Influence of forming history data on required clamping forces in simulation of (a) steel specimens; (b) aluminum specimens

Deviations of required clamping forces in simulation, using geometry from bending simulation with and without forming history data, with respect to experimental results are visualized in Fig. 4 (a). Results are separated according to bending angle and material. Required clamping forces in simulations are evaluated according to experimental results using the mean value of the 56 mm region of pressure film sensor. The average clamping force deviation of three specimens for each configuration is displayed with standard deviation. Further, mean values of experimental



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clamping force and percentage deviation between simulation and experimental result are shown in the table in the lower section. As shown in Fig. 3, the influence of forming history data is much higher for steel specimens, compared to aluminum specimens. This can be observed for all configurations. Taking forming history into account, required clamping forces are reduced. In that way, deviation of simulation results can be decreased significantly. Deviation between results with and without forming history data is due to residual stresses, plastic strains and mainly to the Bauschinger effect. Taking stresses and strains into account, clamping force of steel specimen 1 is reduced by 7.08 % and of aluminum specimen 1 by 2.68 %. When considering the Bauschinger effect in material cards, clamping force of steel specimen 1 is reduced by 32.25 %, and of aluminum specimen 1 by 8.25 %. As reduction of sheet thickness is very low, it has no measurable influence on clamping force. A deviation between the configuration of aluminum 91.94° and the other results can be observed in this and the following diagrams. There were several clamping runs with only partially closed gap. Clamping force was increased until the gap was completely closed. Specimens of this configuration were plastically deformed to a higher level in the flange area, compared to other configurations. This procedure resulted in comparatively high clamping forces in the experiment. In Fig. 4 (b) differences of force are compared with experiments between numerically manufactured specimen geometry and measured specimen geometry. A good agreement of both results can be observed. There are only slight differences, which can be seen for steel 91.35° and aluminum 91.94°. These differences are related to small geometrical deviations in the radius area of specimens. The use of bending simulation for manufacturing specimens numerically is permissible in the regarded case. CR340LA

b

AA5182

150 N

force deviation to experiment

force deviation to experiment

a

100 N 50 N 0N -50 N -100 N -150 N

forming geometry forming geometry with history data n=3

CR340LA

AA5182

150 N 100 N 50 N 0N -50 N -100 N -150 N

forming geometry measured geometry n=3

bending angle

90.84°

91.35°

91.78°

90.82°

91.35°

91.94°

bending angle

90.84°

91.35°

91.78°

90.82°

91.35°

experimental force

258 N

313 N

397 N

326 N

494 N

737 N

experimental force

258 N

313 N

397 N

326 N

494 N

737 N

forming geo

19.7 %

38.98 %

31.52 %

22.03 %

13.38 %

-7.72 %

forming geo

19.7 %

38.98 %

31.52 %

22.03 %

13.38 %

-7.72 %

forming hist

-9.68 %

-4.21 %

1.31 %

12.52 %

3.93 %

-14.42 %

measured geo

15.83 %

44.05 %

31.37 %

21.87 %

14.45 % -13.43 %

91.94°

Fig. 4. Comparison of required clamping force deviation between simulation and experimental results (a) regarding the influence of forming history data; (b) regarding the agreement of specimens from numerically manufactured geometry and measured geometry CR340LA

b

AA5182

150 N

force deviation to experiment

force deviation to experiment

a 100 N 50 N 0N -50 N -100 N -150 N

measured geometry with mapped history data measured geometry n=3

CR340LA

AA5182

150 N 100 N 50 N 0N -50 N -100 N -150 N

measured geometry with mapped history data forming geometry with history data n=3

bending angle

90.84°

91.35°

91.78°

90.82°

91.35°

91.94°

bending angle

90.84°

91.35°

91.78°

90.82°

91.35°

experimental force

258 N

313 N

397 N

326 N

494 N

737 N

experimental force

258 N

313 N

397 N

326 N

494 N

737 N

mapped hist

3.06 %

24.25 %

18.29 %

18.6 %

9.88 %

-13.33 %

mapped hist

3.06 %

24.25 %

18.29 %

18.6 %

9.88 %

-13.33 %

measured geo

15.83 %

44.05 %

31.37 %

21.87 %

14.45 % -13.43 %

forming hist

-9.68 %

-4.21 %

1.31 %

12.52 %

3.93 %

-14.42 %

91.94°

Fig. 5. Comparison of required clamping force deviation between simulation and experimental results (a) regarding the influence of mapped history data; (b) regarding the agreement of specimens from measured geometry with mapped history data and numerically manufactured ones

The influence of mapped history data on the deviation between simulation and experimental results is visualized in Fig. 5 (a). Results of measured geometry are compared to measured geometry with mapped forming history data

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Felix Bauer et al. / Procedia Engineering 207 (2017) 1599–1604 Felix Bauer et al. / Procedia Engineering 00 (2017) 000–000

of numerically manufactured specimens. Mapped forming history data takes results of simulation closer to the experimental results, according to the comparison of Fig. 4 (a). The influence of mapped forming history data is less than the influence of forming history data from forming simulation result. This is visualized in Fig. 5 (b) by comparison of simulation with experiment, using measured geometry including mapped forming history data and forming geometry with history data. Deviation between simulation results can be related mainly to the elimination of history variables in the *INITIAL_STRESS card during mapping process, including backstress components for calculation of kinematic hardening by Chaboche’s law. Thus, influence of forming history data is reduced to residual stresses and plastic strains. Further, there are some minor effects of springback compensation and mapping. 5. Conclusion In this contribution four different setups of a process simulation model for calculation of clamping process with running clamping technology to predict required clamping forces were investigated. FE-simulation results were compared to experiments with L-specimens, manufactured by air bending using the steel grade CR340LA and the aluminum alloy AA5182. Specimens were manufactured with defined geometrical inaccuracies using three selected bending angles. During experiments, specimens underwent a load reversal, compared to previous bending process. Thus, kinematic hardening and residual stresses had a significant influence on required clamping forces. For FE-simulation, specimens were generated by optical measurement of experimental specimens, bending simulation with and without forming history data and by mapping of forming history data on measurement based specimens. The applied mapping method used a springback compensation, which enabled mapping of residual stresses on a slightly different geometry, without distortion of the part due to springback effects. During mapping process, history variables were eliminated. This led to a lower influence of mapped history data, since kinematic hardening was calculated using history variables. Deviations with respect to experiments were compared between different simulation setups. The influence of forming history on required clamping forces of steel specimens (up to 35 %) was much higher than for aluminum specimens (up to 10 %). Significant improvement of simulation results was obtained by applying mapped forming history data. Furthermore, best agreement between clamping simulation and experiment was achieved using numerically manufactured specimens including forming history data. 6. References [1] S. MirRashed, M.R. Mehr, M. Missler-Behr, A. Luder, Analyzing the causes and effects of complexity on different levels of automobile manufacturing systems, in: 21th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, (2016). [2] N. Wemhöner, Flexibilitätsoptimierung zur Auslastungssteigerung im Automobilrohbau. Techn. Hochsch. Dissertation. Aachen, (2005). [3] J. Buehrle, M. Bea, R. Brockmann, Laser Remote Process Technology on Automotive Manufacture, in: Proceedings of the FISITA 2012 World Automotive Congress: Volume 11: Advanced Vehicle Manufacturing Technology, Springer, Berlin, Heidelberg (2013) 89-97. [4] P. Rippl, Industrieroboter zum Laserstrahlschweißen und -schneiden in der Fahrzeugindustrie, Europ. Laser Marketplace 94 (1994) 216-230. [5] M. Ono, Y. Shinbo, A. Yoshitake, M. Ohmura, Development of laser-arc hybrid welding, NKK TECHNICAL REPORT-JAPANESE EDITION (2002) 70-74. [6] F. Bauer, A. Werber, T. Seiberlich, M. Merklein, Process simulation model of a flexible clamping technology for sheet metal parts, 17th International Conference on Sheet Metal, Procedia Engineering 183C (2017) 303-308. [7] J. Bauschinger, Mittheilung 15: Über die Veränderung der Elastizitätsgrenze und Festigkeit des Eisens und Stahls durch Strecken und Quetschen, durch Erwärmen und Abkühlen und durch oftmals wiederholte Beanspruchung, München (1886). [8] J.L. Chaboche, G. Rousselier, On the Plastic and Viscoplastic Constitutive Equations-Part I: Rules Developed With Internal Variable Concept, J. Pressure Vessel Technol. 105 (1983) 153-158. [9] F. Yoshida, T. Uemori, A model of large-strain cyclic plasticity describing the Bauschinger effect and workhardening stagnation, International Journal of Plasticity 18 (2002) 661-686. [10] A. Oeckerath, K. Wolf, Improved Product Design Using Mapping In Manufacturing Process Chains, 10. LS-DYNA Forum, Bamberg (2010) 23-32. [11] M. Rosenschon, S. Suttner, M. Merklein, Evaluation of kinematic hardening models for multiple stress reversals under continuous cyclic shearing and multi-step bending, 10th European LS-DYNA Conference 2015, Würzburg, Germany (2015). [12] M. Wieland, M. Kaupper, M. Merklein, Umform- und Rückfederungssimulation von Leichtbauwerkstoffen - Vergleichende Betrachtung von Zug-Druck- und Wechselbiegeversuchen zur Berücksichtigung des Bauschinger-Effekts, Tagungsband des 33. EFB-Kolloquiums Blechverarbeitung (2013) 347-357. [13] F. Bauer, A. Werber, T. Seiberlich, M. Merklein, Untersuchung einer flexiblen Spanntechnik für das Laserschweißen, Sächsische Fachtagung für Umformtechnik (2016) 72-81.